Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry

Nontarget screen and identify sulfate and sulfonate surfactants in personal care products using UHPLC-Q-Orbitrap-HRMS based on fragmentation characteristics and sulfur isotopologue pattern

Sulfate and sulfonate compounds are extensively used as anionic surfactants in personal care products (PCPs), which might pose adverse potential to human health. However, available research mostly identified certain subsets of sulfated and sulfonated surfactants based on target analysis. In this study, we developed a comprehensive nontarget strategy for identification of sulfated and sulfonated surfactants in PCPs using UHPLCHRMS supplemented by an in-lab R script based on characteristic fragment ions and sulfur isotope patterns. A total of 20 sulfate and 12 sulfonate surfactants of confidence level 3 and above were identified in the range of alkyl chain length from C12 to C26 with 0-7 ethoxy groups and molecular weights of 200-600 Da in the PCP samples. The sulfates included 4 alkyl sulfates and 16 alkyl ether sulfates. In addition to commonly reported 4 alkyl benzene sulfonates, this study identified eight sulfonate surfactants for the first time, which were 3 alkyl sulfonates, 3 methyl ammonium sulfonates, and 2 bis-sulfonate sulfonates in the PCPs. Interestingly, 22 sulfate and sulfonate compounds were identified in the negatively labeled PCP samples which were not supposed to contain sulfate and sulfonate surfactants. The results demonstrated robustness of the developed nontarget analyzing strategy in identifying and characterizing sulfate and sulfonate surfactants and consequently providing guidance for management and regulation of chemical addition in PCPs to ensure safe use.

Isolation, Structure Elucidation and Biological Evaluation of Lomaiviticins F-H, Dimeric Benzofluorene Glycosides from Marine-Derived Micromonospora sp. Bacterium

The discovery of new natural products remains a cornerstone of therapeutic innovation, and effective analytical tools for rapid dereplication can significantly accelerate this process. Using Isotopic Fine Structure (IFS) mass spectrometry, we rapidly identified three new dimeric benzofluorene glycosides, lomaiviticins F-H (1-3), from a marine-derived Micromonospora sp. bacterium. These compounds were isolated and structurally elucidated through advanced spectroscopic techniques, including FT-ICR-MS and NMR. Lomaiviticins F-H exhibit unique structural features, notably the 4-O-methyl-l-angolosamine moieties, which differentiate them from previously known lomaiviticins A-E. The discovery of these compounds highlights distinct biosynthetic linkages within the lomaiviticin family, particularly the C2-C2' conjoining bonds characteristic of the dimers. Compounds 1-3 were evaluated for in vitro cytotoxicity against a panel of human cancer cell lines; the resulting IC50 values confirmed that the dimeric diazofluorenes of lomaiviticins A and B are critical for anticancer activity. These findings emphasize the utility of IFS in expediting natural product discovery while providing valuable insights into structural and functional characterizations of bioactive compounds.

Functional characterization reveals the importance of Arabidopsis ECA4 and EPSIN3 in clathrin mediated endocytosis and wall structure in apical growing cells

Localized clathrin mediated endocytosis is vital for secretion and wall deposition in apical growing plant cells. Adaptor and signalling proteins, along with phosphoinositides, are known to play a regulatory, yet poorly defined role in this process. Here we investigated the function of Arabidopsis ECA4 and EPSIN3, putative mediators of the process, in pollen tubes and root hairs. Homozygous eca4 and epsin3 plants exhibited altered pollen tube morphology (in vitro) and self-pollination led to fewer seeds and shorter siliques. These effects were augmented in eca4/epsin3 double mutant and quantitative polymerase chain reaction data revealed changes in phosphoinositide metabolism and flowering genes suggestive of a synergistic action. No visible changes were observed in root morphology, but atomic force microscopy in mutant root hairs showed altered structural stiffness. Imaging and FRET-FLIM analysis of ECA4 and EPSIN3 X-FP constructs revealed that both proteins interact at the plasma membrane but exhibit slightly different intracellular localization. FT-ICR-MS metabolomic analysis of mutant cells showed changes in lipids, amino acids and carbohydrate composition consistent with a role in secretion and growth. Characterization of double mutants of eca4 and epsin3 with phospholipase C genes (plc5, plc7) indicates that phosphoinositides (e.g. PtdIns(4,5)P2) are fundamental for a combined and complementary role of ECA4-EPSIN3 in cell secretion.

Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Molecular-Level Insights into Recalcitrant Ozonation Products from Effluent Organic Matter

Wastewater ozonation is commonly employed to enhance the subsequent biodegradation of effluent organic matter (EfOM) and contaminants of concern. However, there is evidence suggesting the formation of recalcitrant ozonation products (OPs) from EfOM. To investigate the biodegradability of OPs we conducted batch biodegradation experiments using wastewater effluent ozonated with mass-labeled (18O) ozone. Molecular level analysis of EfOM was performed using reversed-phase liquid chromatography coupled with Fourier transform ion cyclotron resonance mass spectrometry (LC-FT-ICR MS) after 3 and 28 days in batch bioreactors. The use of mass labeling allowed for the unambiguous detection of OPs, with 2933 labeled OP features identified in the ozonated EfOM. Furthermore, employing polarity separation with LC facilitated the investigation of reactivity among different OP isomers. Overall, OPs exhibited a comparable proportion of recalcitrant and bioproduced molecular formulas when compared to the remaining EfOM, with 12% of OPs classified as recalcitrant and 17% bioproduced, indicating that OPs themselves are not inherently biodegradable. Additionally, recalcitrant OPs were found to be more polar based on the O/C ratios and retention time, in comparison to biodegradable OPs. Approximately one-third of the OP isomers displayed variations in their biodegradability, suggesting that studying the degradability of ozonated EfOM at the molecular formula level is heavily influenced by structural differences. Here, we offer unique insight into the biological transformation of EfOM after ozonation using labeled ozone and LC-FT-ICR MS analysis.

The isotope distribution: A rose with thorns

The isotope distribution, which reflects the number and probabilities of occurrence of different isotopologues of a molecule, can be theoretically calculated. With the current generation of (ultra)-high-resolution mass spectrometers, the isotope distribution of molecules can be measured with high sensitivity, resolution, and mass accuracy. However, the observed isotope distribution can differ substantially from the expected isotope distribution. Although differences between the observed and expected isotope distribution can complicate the analysis and interpretation of mass spectral data, they can be helpful in a number of specific applications. These applications include, yet are not limited to, the identification of peptides in proteomics, elucidation of the elemental composition of small organic molecules and metabolites, as well as wading through peaks in mass spectra of complex bioorganic mixtures such as petroleum and humus. In this review, we give a nonexhaustive overview of factors that have an impact on the observed isotope distribution, such as elemental isotope deviations, ion sampling, ion interactions, electronic noise and dephasing, centroiding, and apodization. These factors occur at different stages of obtaining the isotope distribution: during the collection of the sample, during the ionization and intake of a molecule in a mass spectrometer, during the mass separation and detection of ionized molecules, and during signal processing.

Metabolomics combined with network pharmacology revealed a paradigm for determining the mechanism underlying the metabolic action of Gegen Qinlian Decoction amelioration of ulcerative colitis in mice

Ulcerative colitis (UC) is a common disease of the digestive system that is challenging to treat. Gegen Qinlian Decoction (GQD), which is an ancient classic formula in Chinese medicine, is effective at alleviating the symptoms of UC, but comprehensive research on its mechanism of action has not been performed. Here, we explored the material basis and potential molecular mechanism underlying GQD-mediated protection against UC by integrated metabolomics and network pharmacology. First, differentially expressed metabolites were screened and identified via a metabolomics approach, and the metabolic pathway was analyzed via MetaboAnalyst. Second, a protein-protein interaction (PPI) network was constructed to identify hub genes that encode metabolic enzymes. Third, the differentially expressed metabolites were used to construct a compound-reaction-enzyme-gene network. Finally, the metabolites were compared with relevant active components for molecular docking, molecular dynamics (MD) simulation, and verification experiment. GQD intervention alleviated UC in mice and significantly inhibited metabolic dysfunction in mice with UC; specifically, GQD reversed the abnormal changes in metabolites in the colon and serum, and regulated the arachidonic acid metabolism, tryptophan metabolism, glycerophospholipid metabolism, and purine metabolism pathways. Further literature review and molecular docking analysis with targeted MD simulation and Poisson-Boltzmann surface area (MM-PBSA) analysis were performed, revealing that GQD may inhibit the disruption of arachidonic acid metabolism and tryptophan metabolism by suppressing PTGS2 and CYP450 protein expression; these results were verified by qRT-PCR, WB, and surface plasmon resonance (SPR) assays. Our experiments indicated that GQD alleviated UC in mice by systematically regulating arachidonic acid metabolism and tryptophan metabolism, supporting further research and the development of GQD as a novel drug for ameliorating UC.

Early detection of bacterial pneumonia by characteristic induced odor signatures

INTRODUCTION

The ability to detect pathogenic bacteria before the onsets of severe respiratory symptoms and to differentiate bacterial infection allows to improve patient-tailored treatment leading to a significant reduction in illness severity, comorbidity as well as antibiotic resistance. As such, this study refines the application of the non-invasive Secondary Electrospray Ionization-High Resolution Mass Spectrometry (SESI-HRMS) methodology for real-time and early detection of human respiratory bacterial pathogens in the respiratory tract of a mouse infection model.

METHODS

A real-time analysis of changes in volatile metabolites excreted by mice undergoing a lung infection by Staphylococcus aureus or Streptococcus pneumoniae were evaluated using a SESI-HRMS instrument. The infection status was confirmed using classical CFU enumeration and tissue histology. The detected VOCs were analyzed using a pre- and post-processing algorithm along with ANOVA and RASCA statistical evaluation methods.

RESULTS

Characteristic changes in the VOCs emitted from the mice were detected as early as 4-6 h post-inoculation. Additionally, by using each mouse as its own baseline, we mimicked the inherent variation within biological organism and reported significant variations in 25 volatile organic compounds (VOCs) during the course of a lung bacterial infection.

CONCLUSION

the non-invasive SESI-HRMS enables real-time detection of infection specific VOCs. However, further refinement of this technology is necessary to improve clinical patient management, treatment, and facilitate decisions regarding antibiotic use due to early infection detection.

Target, suspect, and non-target screening of per- and poly-fluoroalkyl substances in wastewater treatment plant effluents in South Korea using ion mobility spectrometry-mass spectrometry

This study used target, suspect, and non-target screening, to assess the presence of per- and polyfluoroalkyl substances (PFASs) in domestic (municipal) and industrial wastewater treatment plants (WWTPs) in South Korea. Target analysis quantified 20 PFASs in the WWTP effluents. Total concentration of PFASs ranged from 69.1 to 79.6 ng/L and the concentrations of perfluorobutanoic acid (PFBA) (mean: 15.6 ng/L, median: 14.9 ng/L) and perfluorooctanoic acid (PFOA) (mean: 14.7 ng/L, median: 12.7 ng/L) were higher than those of other PFASs. Compared to 2010, there was an overall increase in perfluoroalkyl carboxylic acids (PFCAs), particularly perfluoroheptanoic acid, (PFHpA), which showed a nearly 10-fold increase, with current concentrations reaching 9.5 ng/L. Suspect and non-target screening with ion mobility spectrometry (IMS)-mass spectrometry was used to identify additional PFASs based on their exact mass, collision cross-section (CCS), and tandem mass spectrometry fragmentation patterns. Twenty compounds were identified as PFAS compounds through suspect screening at a confidence level (CL) of 3 or higher, with five compounds identified at CL 2. Additionally, fragment-based, suspect and non-target screening identified emerging PFASs, including FBSA, a n:2 fluorotelomer-based non-polymer, and bistriflimide, all with CL 2. Semi-quantification of identified PFASs revealed that the concentrations of PFASs identified by suspect and non-target screening were higher than those of the target PFASs, especially in industrial wastewater effluents.

Molecular breath profile of acute COPD exacerbations

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) show high variability in individual susceptibility and promote disease progression; thus, accurate diagnosis and treatment is essential. Unravelling the molecular metabolic changes during AECOPD in breath could promote understanding of AECOPD and its treatment. Our objective was to investigate the metabolic breath profiles during AECOPD for biomarker detection. We conducted real-time breath analysis in patients with COPD during AECOPD and during subsequent stable phase. Molecular breath patterns were compared between AECOPD and stable phase by dimension reduction techniques and paired t-tests. Pathway enrichment analyses were performed to investigate underlying metabolic pathways. Partial least-squares discriminant analysis and XGboost were utilised to build a prediction model to differentiate AECOPD from stable state. 35 patients (60% male) with a mean age of 65 (10.2) yr with AECOPD were included. AECOPD could be predicted with a high sensitivity of 82.5% (95% confidence interval of 68.8%-93.8%) and an excellent discriminative power (AUC = 0.86). Metabolic changes in the linoleate, tyrosine, and tryptophan pathways during AECOPD were predominant. Significant metabolic changes occur during COPD exacerbations, predominantly in the linoleate, tyrosine, and tryptophan pathways, which are all linked to inflammation. Real-time exhaled breath analysis enables a good prediction of AECOPD compared to stable state and thus could enhance precision of AECOPD diagnosis and efficacy in clinical practice.

Effects of relative humidity on atmospheric organosulfur species derived from photooxidation and nocturnal chemistry in a forest environment

The molecular composition of organic aerosols in ambient PM2.5 was investigated at the northern foothills of Qinling Mountain region in central China during the summer of 2022. The molecular characteristics of organic matter were analyzed using ultrahigh performance liquid chromatography coupled with high-resolution Orbitrap mass spectrometry. The number of molecular formula assignments was dominated by organosulfur species (OrgS, on average 28-47% in total). Both the number and peak area of OrgS increased significantly with higher relative humidity (RH), while those of CHO and CHON species decreased, indicating the different impacts of RH on individual organic groups. The entire study period was divided into two distinct stages, including a low RH and a high RH period (LRH and HRH). The enhanced formation of CHOS with O>7 in HRH was primarily attributed to photochemical oxidation, whereas the increase in CHONS with O>7 was driven by enhanced NO3 radical-initiated oxidation under elevated RH conditions. Nearly 50% of OrgS presented only in HRH had aliphatic structures with C≥9. Although isoprene-derived organosulfates dominated the peak area across the entire period, the monoterpenes and long-chain alkanes-derived organosulfates largely increased in HRH. The strong correlations between aerosol liquid water content and OrgS containing high oxygen content (O>7) indicate that aqueous-phase reactions played a crucial role in multigenerational oxidation of OrgS. This study highlights the important effects of RH on the formation of OrgS, particularly for organosulfates derived from monoterpenes and long-chain alkanes.

Data Processing of Product Ion Spectra: Methods to Control False Discovery Rate in Compound Search Results for Untargeted Metabolomics

Several database search methods have been employed in untargeted metabolomics utilizing high-resolution mass spectrometry to comprehensively annotate acquired product ion spectra. Recent technical advancements in in silico analyses have facilitated the sorting of the degree of coincidence between a query product ion spectrum, and the molecular structures in the database. However, certain search results may be false positives, necessitating a method for controlling the false discovery rate (FDR). This study proposes 4 simple methods for controlling the FDR in compound search results. Instead of preparing a decoy compound database, a decoy spectral dataset was created from the measured product-ion spectral dataset (target). Target and decoy product ion spectra were searched against an identical compound database to obtain target and decoy hits. FDR was estimated based on the number of target and decoy hits. In this study, 3 decoy generation methods, polarity switching, mirroring, and spectral sampling, were compared. Additionally, the second-rank method was examined using second-ranked hits in the target search results as decoy hits. The performances of these 4 methods were evaluated by annotating product ion spectra from the MassBank database using the SIRIUS 5 CSI:FingerID scoring method. The results indicate that the FDRs estimated using the second-rank method were the closest to the true FDR of 0.05. Using this method, a compound search was performed on 4 human metabolomic data-dependent acquisition datasets with an FDR of 0.05. The FDR-controlled compound search successfully identified several compounds not present in the Human Metabolome Database.

Untargeted and Targeted Lipidomics Unveil Dynamic Lipid Metabolism Alterations in Type 2 Diabetes

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a complex metabolic disorder with a growing body of evidence suggesting the central role of lipid metabolism in its pathogenesis. However, the dynamic changes in lipid metabolism across different stages of T2DM remain understudied.

OBJECTIVE

This study aimed to elucidate the temporal alterations in lipid metabolism in T2DM using an integrated lipidomics approach.

METHOD

Serum samples from 155 subjects were analyzed using LC-MS-based lipidomics, including untargeted and targeted approaches.

RESULTS

We identified significant alterations in 44 lipid metabolites in newly diagnosed T2DM patients and 29 in high-risk individuals, compared with healthy controls. Key metabolic pathways such as sphingomyelin, phosphatidylcholine, and sterol ester metabolism were disrupted, highlighting the involvement of insulin resistance and oxidative stress in T2DM progression. Moreover, 13 lipid metabolites exhibited diagnostic potential for T2DN, showing consistent trends of increase or decrease as the disease progressed.

CONCLUSION

Our findings underscore the importance of lipid metabolism in T2D development and identify potential lipid biomarkers for early diagnosis and monitoring of disease progression, which contribute to paving the way for novel therapeutic strategies.

Comparative analysis of feature annotation methods for SESI-HRMS in exhaled breath analysis

Secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS) is a powerful method for the analysis of exhaled breath in real time. However, feature annotation is challenging due to the flow-injection nature of the technique. To evaluate alternative methods for enhancing feature annotation, a study was conducted where the exhaled breath of sixteen subjects was condensed and analyzed using dynamic headspace vacuum in-trap extraction gas chromatography-mass spectrometry (DHS-V-ITEX-GC-MS) and liquid chromatography coupled to mass spectrometry (LC-MS) using polar and reverse-phase conditions along with a data-independent MS2-acquisition method based on multiple injections. The annotation results obtained from these methods were compared to those from SESI-HRMS. The use of these techniques on breath condensate is unprecedented. The GC-MS method primarily detected compounds of exogenous origin, particularly additives in oral hygiene products like menthol. On the other hand, LC-MS detected a vast number of features, especially with the utilized data-independent acquisition method. Chemical classes to these features were assigned in-silico. In positive ion mode, mostly amino acids and amines were detected, while the largest group in negative ion mode consisted of carboxylic acids. Approximately 25% and 5% of SESI features had a corresponding match with LC-MS and GC-MS. While both GC-MS and LC-MS methods partially overlapped with the SESI features, there was limited overlap of both in the mass-to-charge range from 150 to 200. In conclusion, both GC-MS and LC-MS analysis of breath condensate can serve as supplementary tools for annotating features obtained from SESI-MS. However, to increase confidence in the annotation results, combining these methods with additional on-line fragmentation techniques is recommended.

Effects of photochemical aging on the molecular composition of organic aerosols derived from agricultural biomass burning in whole combustion process

Biomass burning organic aerosols (BBOA) are key components of atmospheric particulate matter, yet the effects of aging process on their chemical composition and related properties remain poorly understood. In this study, fresh smoke emissions from the combustion of three types of agricultural biomass residues (rice, maize, and wheat straws) were photochemically aged in an oxidation flow reactor. The changes in BBOA composition were characterized by offline analysis using ultrahigh performance liquid chromatography coupled with Orbitrap mass spectrometry. The BBOA molecular composition varied dramatically with biomass type and aging process. Fresh and aged BBOA were predominated by CHO and nitrogen-containing CHON, CHN, and CHONS species, while with very few CHOS and other non‑oxygen species. The signal peak area variations revealed that individual molecular species underwent dynamic changes, with 77-81 % of fresh species decreased or even disappeared and 33-46 % of aged species being newly formed. A notable increase was observed in the number and peak area of CxHyO≥6 compounds in aged BBOA, suggesting that photochemical process served as an important source of highly oxygenated species. Heterocyclic CxHyN2 compounds mostly dominated in fresh CHN species, whereas CxHyN1 were more abundant in aged ones. Fragmentation and homologs oxidation by addition of oxygen-containing functional groups were important pathways for the BBOA aging. The changes in BBOA composition with aging would have large impacts on particle optical properties and toxicity. This study highlights the significance of photochemical aging process in altering chemical composition and related properties of BBOA.

Molecular fractionation mediates genotoxicity evolution of hydrochar-derived dissolved organic matter at the iron oxyhydroxides-water interface

Adsorption fractionation of dissolved organic matter (DOM) induced by soil minerals is a common geochemical process, which has been widely documented on natural DOM. Hydrochar is a promising functional material in soil remediation but can continuously release abundant endogenic DOM with potential biotoxicity. However, adsorption fractionation at molecular level and its influence on toxicity evolution of hydrochar-derived DOM (HDOM) at genetic level at the soil-water interface remain poorly understood. Herein, we investigated the molecular fractionation of HDOM on three typical soil iron minerals (i.e., ferrihydrite, goethite, and hematite). Results from ultrahigh-resolution mass spectrum showed that HDOM molecules with high molecular weight and high contents of unsaturated oxidized or aromatic structures (e.g., unsaturated phenolic compounds, polyphenols, and organic acids) were preferentially absorbed by iron oxyhydroxides, while aliphatic molecules and poorly oxygenated compounds (e.g., hydrocarbon, phenols, and alcohols) were retained in aqueous phase. Furthermore, we quantitatively evaluated their genotoxicity variation using a toxicogenomics assay using green fluorescence protein-fused whole-cell array, and results showed that oxidative, protein, membrane, and DNA stresses were primary responses upon exposure to original HDOM. Interface fractionation induced by iron oxyhydroxides significantly reduced genotoxicity of HDOM, especially for oxidative, membrane and DNA stresses. Overall, the selective absorption of HDOM molecules by iron oxyhydroxides shifted its biotoxicity, which might change the ecological effects of hydrochar amendment, e.g., microbial community structure, environmental pollutant transformation, and even the ecological function of terrestrial and aquatic ecosystems. These findings would contribute to unraveling the environmental geochemistry process of HDOM in the natural soil-water interface and provide a new insight into the biotoxicity of hydrochar usage to terrestrial and aquatic environments.

GlyCombo Enables Rapid, Complete Glycan Composition Identification across Diverse Glycomic Sample Types

Glycans are sugar-based polymers found to modify biomolecules, including lipids and proteins, as well as occur unconjugated as free polysaccharides. Due to their ubiquitous cellular presentation, glycans mediate crucial biological processes and are frequently sought after as biomarkers for a wide range of diseases. Identification of glycans present in samples acquired with mass spectrometry (MS) is a cornerstone of glycomics research; thus, the ability to rapidly identify glycans in each acquisition is integral to glycomics analysis pipelines. Here we introduce GlyCombo (https://github.com/Protea-Glycosciences/GlyCombo), an open-source, freely available software tool designed to rapidly assign monosaccharide combinations to glycan precursor masses including those subjected to MS2 in LC-MS/MS experiments. GlyCombo was evaluated across six diverse data sets, demonstrating MS vendor, derivatization, and glycan-type neutrality. Compositional assignments using GlyCombo are shown to be faster than the current predominant approach, GlycoMod, a closed-source web application. Two unique features of GlyCombo, multiple adduct search and off-by-one error anticipation, reduced unassigned MS2 scans in a benchmark data set by 40%. Finally, the comprehensiveness of glycan feature identification is exhibited in Skyline, a software that requires predefined transitions that are derived from GlyCombo output files.

Biological and metabolomics-guided isolation of tetrahydrofurofuran lignan from Croton spp. with antiproliferative activity against human melanoma cell line

The Croton genus (Euphorbiaceae) is recognized as a promising source for identifying bioactive compounds with antiproliferative activity. However, knowledge on the chemical composition and activity of Croton floribundus, Croton echinocarpus, and Croton zehntneri is limited. Thus, this study aimed to investigate the antiproliferative activity of these species on cells derived from tumoral breast, lung, and melanoma cells, and primary fibroblasts derived from human skin. Metabolomic strategies were applied via ultra-performance liquid chromatography coupled with high-resolution mass spectrometry and multivariate statistical analysis to target the main active compound. The C. floribundus leaf extract exhibited the highest activity, with an IC50 value lower than that of the reference drug - temozolomide - in the most responsive cell line - SK-MEL-147 - and in all the evaluated melanoma cell lines (SK-MEL-147, CHL-1 and WM-1366). Four tetrahydrofurofuran lignans were isolated for the first time from the most promising fraction of the C. floribundus extract. According to the metabolomic and multivariate statistical analyses, the isolated lignan epi-yangambin constituted the main antiproliferative compound against SK-MEL-147; furthermore, it exhibited selective antiproliferative activity for this cell line (IC50 = 13.09 μg/mL and selectivity index = 3.82; temozolomide, IC50 = 121.50 μg/mL) due to, at least in part, its ability to inhibit cell cycle progression at G2/M. This is especially relevant considering the high resistance of melanoma cells to available drugs. Thus, epi-yangambin can serve as a prototype for further antiproliferative investigations.

How aromatic dissolved organic matter differs in competitiveness against organic micropollutant adsorption

Activated carbon is employed for the adsorption of organic micropollutants (OMPs) from water, typically present in concentrations ranging from ng L-1 to μg L-1. However, the efficacy of OMP removal is considerably deteriorated due to competitive adsorption from background dissolved organic matter (DOM), present at substantially higher concentrations in mg L-1. Interpreting the characteristics of competitive DOM is crucial in predicting OMP adsorption efficiencies across diverse natural waters. Molecular weight (MW), aromaticity, and polarity influence DOM competitiveness. Although the aromaticity-related metrics, such as UV254, of low MW DOM were proposed to correlate with DOM competitiveness, the method suffers from limitations in understanding the interplay of polarity and aromaticity in determining DOM competitiveness. Here, we elucidate the intricate influence of aromaticity and polarity in low MW DOM competition, spanning from a fraction level to a compound level, by employing direct sample injection liquid chromatography coupled with ultrahigh-resolution Fourier-transform ion cyclotron resonance mass spectrometry. Anion exchange resin pre-treatment eliminated 93% of UV254-active DOM, predominantly aromatic and polar DOM, and only minimally alleviated DOM competition. Molecular characterization revealed that nonpolar molecular formulas (constituting 26% PAC-adsorbable DOM) with medium aromaticity contributed more to the DOM competitiveness. Isomer-level analysis indicated that the competitiveness of highly aromatic LMW DOM compounds was strongly counterbalanced by increased polarity. Strong aromaticity-derived π-π interaction cannot facilitate the competitive adsorption of hydrophilic DOM compounds. Our results underscore the constraints of depending solely on aromaticity-based approaches as the exclusive interpretive measure for DOM competitiveness. In a broader context, this study demonstrates an effect-oriented DOM analysis, elucidating counterbalancing interactions of DOM molecular properties from fraction to compound level.

High-Resolution Tandem Mass Spectrometry-Based Analysis of Model Lignin-Iron Complexes: Novel Pipeline and Complex Structures

Understanding the chemical nature of soil organic carbon (SOC) with great potential to bind iron (Fe) minerals is critical for predicting the stability of SOC. Organic ligands of Fe are among the top candidates for SOCs able to strongly sorb on Fe minerals, but most of them are still molecularly uncharacterized. To shed insights into the chemical nature of organic ligands in soil and their fate, this study developed a protocol for identifying organic ligands using ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and metabolomic tools. The protocol was used for investigating the Fe complexes formed by model compounds of lignin-derived organic ligands, namely, caffeic acid (CA), p-coumaric acid (CMA), vanillin (VNL), and cinnamic acid (CNA). Isotopologue analysis of 54/56Fe was used to screen out the potential UHPLC-HRMS (m/z) features for complexes formed between organic ligands and Fe, with multiple features captured for CA, CMA, VNL, and CNA when 35/37Cl isotopologue analysis was used as supplementary evidence for the complexes with Cl. MS/MS spectra, fragment analysis, and structure prediction with SIRIUS were used to annotate the structures of mono/bidentate mono/biligand complexes. The analysis determined the structures of monodentate and bidentate complexes of FeLxCly (L: organic ligand, x = 1-4, y = 0-3) formed by model compounds. The protocol developed in this study can be used to identify unknown organic ligands occurring in complex environmental samples and shed light on the molecular-level processes governing the stability of the SOC.

Selective Profiling of Carboxylic Acid in Crude Oil by Halogen Labeling Combined with Liquid Chromatography and High-Resolution Mass Spectrometry

Carboxylic acids are a small but essential compound class within petroleum chemistry, influencing crude oil behaviors in production and processing and causing environmental impacts. Detailed structural information is fundamental to understanding their influence on petroleum characteristics. However, characterizing acids in crude oil remains challenging due to matrix effects, structural diversity, and low abundance. In this work, we present a new methodology for profiling carboxylic acids by liquid-liquid extraction and selective derivatization using 4-bromo-N-methylbenzylamine (4-BNMA) followed by liquid chromatography and electrospray ionization Orbitrap mass spectrometry (LC-ESI-Orbitrap MS). The fragmentation of 4-BNMA derivatives produces a unique product ion pair, m/z 169/171, enabling the identification of chromatographic fractions containing carboxylic acids. The mass spectra of the corresponding fractions are extracted, and the acids are further computationally isolated based on the isotopic pattern. The method was optimized and validated using acid standards and systematic experimental designs, assuring robustness and sensitivity for nontarget screening purposes. This method detected up to 380 carboxylic acids in six Danish North Sea crude oils, with up to two carboxyl and other heteroatom functionalities (NSO). The results indicated that the most populated species are fatty acids (double bond equivalent (DBE) = 1) and small aromatic acids (DBE = 2-6). The predominance and diversities of compound classes in different samples are consistent with their corresponding bulk properties. Polyfunctional acids (Ox, NxOx, and SxOx) were observed due to exposure to oxidation and biodegradation. Also, the approach's applicability benefits high-resolution MS analysis by simplifying data processing for crude oil and potentially other high-organic and aqueous samples.

Discovery and identification of natural alkaloids with potential to impact insulin resistance syndrome in Cyclocarya paliurus. (Batal) leaves by UPLC-QTOF-MS combined with HepG2 cells

Cyclocarya paliurus (Batal.) leaves, which contain a range of bioactive compounds, have been used as a traditional Chinese medicine homologous food since ancient times. However, there is a paucity of literature on comprehensive studies of alkaloids in the leaves of Cyclocarya paliurus (Batal.). For the first time, this study aimed to discover and identify alkaloids extracted from Cyclocarya paliurus (Batal.) leaves by ultra-high performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry (UPLC-QTOF-MS). A total of ten alkaloids have been identified from Cyclocarya paliurus (Batal.) leaves based on accurate mass spectra (mass accuracy, isotopic spacing and distribution) and comparison to fragmentation spectra reported in the literature. In vitro, alkaloids alleviated insulin resistance by increasing glucose consumption and glycogen content in insulin resistance HepG2 cells. The RNA-seq and western blotting results showed that alkaloids could upregulate the expression of phosphatidylinositol 3-kinase (PI3K), and increase the phosphorylation of insulin receptor protein kinase B (AKT). This study not only clarified the chemical constituents and revealed that diverse alkaloids also presented from Cyclocarya paliurus (Batal.) leaves, also, it will provide chemical information on potential compounds for developing new drugs.

Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

Denoising Search doubles the number of metabolite and exposome annotations in human plasma using an Orbitrap Astral mass spectrometer

Chemical exposures may impact human metabolism and contribute to the etiology of neurodegenerative disorders like Alzheimer's Disease (AD). Identifying these small metabolites involves matching experimental spectra to reference spectra in databases. However, environmental chemicals or physiologically active metabolites are usually present at low concentrations in human specimens. The presence of noise ions can significantly degrade spectral quality, leading to false negatives and reduced identification rates. In response to this challenge, the Spectral Denoising algorithm removes both chemical and electronic noise. Spectral Denoising outperformed alternative methods in benchmarking studies on 240 tested metabolites. It improved high confident compound identifications at an average 35-fold lower concentrations than previously achievable. Spectral Denoising proved highly robust against varying levels of both chemical and electronic noise even with >150-fold higher intensity of noise ions than true fragment ions. For human plasma samples of AD patients that were analyzed on the Orbitrap Astral mass spectrometer, Denoising Search detected 2.3-fold more annotated compounds compared to the Exploris 240 Orbitrap instrument, including drug metabolites, household and industrial chemicals, and pesticides. This combination of advanced instrumentation with a superior denoising algorithm opens the door for precision medicine in exposome research.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Discovery and Identification of Three Homocysteine Metabolites by Chemical Derivatization and Mass Spectrometry Fragmentation

Discovery and identification of a new endogenous metabolite are typically hindered by requirements of large sample volumes and multistage purifications to guide synthesis of the standard. Presented here is a metabolomics platform that uses chemical tagging and tandem mass spectrometry to determine structure, direct synthesis, and confirm identity. Three new homocysteine metabolites are reported: N-succinyl homocysteine, 2-methyl-1,3-thiazinane-4-carboxylic acid (MTCA), and homolanthinone.

Exploring the Chemical Space of the Exposome: How Far Have We Gone?

Around two-thirds of chronic human disease can not be explained by genetics alone. The Lancet Commission on Pollution and Health estimates that 16% of global premature deaths are linked to pollution. Additionally, it is now thought that humankind has surpassed the safe planetary operating space for introducing human-made chemicals into the Earth System. Direct and indirect exposure to a myriad of chemicals, known and unknown, poses a significant threat to biodiversity and human health, from vaccine efficacy to the rise of antimicrobial resistance as well as autoimmune diseases and mental health disorders. The exposome chemical space remains largely uncharted due to the sheer number of possible chemical structures, estimated at over 1060 unique forms. Conventional methods have cataloged only a fraction of the exposome, overlooking transformation products and often yielding uncertain results. In this Perspective, we have reviewed the latest efforts in mapping the exposome chemical space and its subspaces. We also provide our view on how the integration of data-driven approaches might be able to bridge the identified gaps.

Formulas of High MW Unknown Compounds from Accurate Mass Differences and Ranking of Best Candidates from First Principles

The number of possible candidate formulas for high molecular weight unknown compounds (e.g., 7000-8000 Da for common 20-mer oligonucleotides) by high-resolution mass spectrometry is in the order of several hundred thousand even at the highest level of experimental accuracy. In demanding analytical applications involving new chemistries and synthetic routes where little is known about the chemical nature or mechanisms of formation of the unknown compounds (e.g., impurities), the generation of a short list of the most plausible formulas would be highly desirable. Such an approach has been developed in the current work. The concept of mass difference from a reference compound is introduced to simplify the approach and greatly reduce the number of possible formulas. The approach allows for the generation of candidate formulas by both the addition and subtraction of atoms to account for all possible molecular changes from the parent compound. A reduction of 3 orders of magnitude in the number of possible formulas has been achieved by the approach. Ranking of the formulas by the product of the sums of the absolute changes in the total number of all atoms and all heteroatoms in the proposed difference formula successfully ranked the correct formula within the top 10 from a list of 200-250 best candidate formulas. There is a tendency for the impurities to be formed involving the least change in the number of atoms and heteroatoms. ΔfHo and ΔfG'o values can be used as a complementary ranking system of the top candidates. The approach is applicable to unknowns in any other systems of high MW compounds.

Occurrence and Distribution of Antibacterial Quaternary Ammonium Compounds in Chinese Estuaries Revealed by Machine Learning-Assisted Mass Spectrometric Analysis

Antimicrobial resistance (AMR) undermines the United Nations Sustainable Development Goals of good health and well-being. Antibiotics are known to exacerbate AMR, but nonantibiotic antimicrobials, such as quaternary ammonium compounds (QACs), are now emerging as another significant driver of AMR. However, assessing the AMR risks of QACs in complex environmental matrices remains challenging due to the ambiguity in their chemical structures and antibacterial activity. By machine learning prediction and high-resolution mass spectrometric analysis, a list of antibacterial QACs (n = 856) from industrial chemical inventories is compiled, and it leads to the identification of 50 structurally diverse antibacterial QACs in sediments, including traditional hydrocarbon-based compounds and new subclasses that bear additional functional groups, such as choline, ester, betaine, aryl ether, and pyridine. Urban wastewater, aquaculture, and hospital discharges are the main factors influencing QAC distribution patterns in estuarine sediments. Toxic unit calculations and metagenomic analysis revealed that these QACs can influence antibiotic resistance genes (particularly sulfonamide resistance genes) through cross- and coresistances. The potential to influence the AMR is related to their environmental persistence. These results suggest that controlling the source, preventing the co-use of QACs and sulfonamides, and prioritizing control of highly persistent molecules will lead to global stewardship and sustainable use of QACs.

Releasing characteristics of toxic chemicals from polystyrene microplastics in the aqueous environment during photoaging process

Microplastics (MPs) are pervasive in the environment and inevitably undergo photoaging due to UV irradiation. This study delved into the dynamic releasing and transformation process of toxic chemicals from polystyrene microplastics (PS MPs) during photoaging, a subject that remains underexplored. It was revealed that photoaging led to substantial alterations in the physicochemical properties of PS MPs, initiating polymer chain scission and facilitating the release of a large number of toxic chemicals, including numerous organic compounds and several inorganic compounds. The kinetic analysis revealed a dynamic release pattern for PS MPs, where under varying UV intensities (2, 5, and 10 mW/cm2), the release rate (kDOC) initially increased and then decreased, peaking at a total irradiation energy of approximately 7 kW·h/m2. Furthermore, chemicals in leachate were transformed into compounds with smaller molecular weight, higher oxidized and greater unsaturated state over the prolonged photoaging. This transformation was primarily attributed to two reasons. Firstly, the aged PS MPs released chemicals with higher oxidized state compared to the pristine MPs. Secondly, the chemicals previously released underwent further reactions. Besides, among the complex leachate generated by aged PS MPs, the organic chemicals characterized by small molecular weight and high oxidized state exhibited notable acute toxicity, whereas heavy metal ions showed lesser toxicity, and anions were non-toxic. This study shed more light on the photoaging process of PS MPs, releasing characteristics of organic chemicals, and the potential environmental risks associated with plastic wastes.

Peatland Wildfires Enhance Nitrogen-Containing Organic Compounds in Marine Aerosols over the Western Pacific

Peatland wildfires contribute significantly to the atmospheric release of light-absorbing organic carbon, often referred to as brown carbon. In this study, we examine the presence of nitrogen-containing organic compounds (NOCs) within marine aerosols across the Western Pacific Ocean, which are influenced by peatland fires from Southeast Asia. Employing ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) in electrospray ionization (ESI) positive mode, we discovered that NOCs are predominantly composed of reduced nitrogenous bases, including CHN+ and CHON+ groups. Notably, the count of NOC formulas experiences a marked increase within plumes from peatland wildfires compared to those found in typical marine air masses. These NOCs, often identified as N-heterocyclic alkaloids, serve as potential light-absorbing chromophores. Furthermore, many NOCs demonstrate pyrolytic stability, engage in a variety of substitution reactions, and display enhanced hydrophilic properties, attributed to chemical processes such as methoxylation, hydroxylation, methylation, and hydrogenation that occur during emission and subsequent atmospheric aging. During the daytime atmospheric transport, aging of aromatic N-heterocyclic compounds, particularly in aliphatic amines prone to oxidation and reactions with amine, was observed. The findings underscore the critical role of peatland wildfires in augmenting nitrogen-containing organics in marine aerosols, underscoring the need for in-depth research into their effects on marine ecosystems and regional climatic conditions.

Effect-directed analysis of androgenic compounds from sewage sludges in China

The safety of municipal sewage sludge has raised great concerns because of the accumulation of large-scale endocrine disrupting chemicals in the sludge during wastewater treatment. The presence of contaminants in sludge can cause secondary pollution owing to inappropriate disposal mechanisms, posing potential risks to the environment and human health. Effect-directed analysis (EDA), involving an androgen receptor (AR) reporter gene bioassay, fractionation, and suspect and nontarget chemical analysis, were applied to identify causal AR agonists in sludge; 20 of the 30 sludge extracts exhibited significant androgenic activity. Among these, the extracts from Yinchuan, Kunming, and Shijiazhuang, which held the most polluted AR agonistic activities were prepared for extensive EDA, with the dihydrotestosterone (DHT)-equivalency of 2.5 - 4.5 ng DHT/g of sludge. Seven androgens, namely boldione, androstenedione, testosterone, megestrol, progesterone, and testosterone isocaproate, were identified in these strongest sludges together, along with testosterone cypionate, first reported in sludge media. These identified androgens together accounted for 55 %, 87 %, and 52 % of the effects on the sludge from Yinchuan, Shijiazhuang, and Kunming, respectively. This study elucidates the causative androgenic compounds in sewage sludge and provides a valuable reference for monitoring and managing androgens in wastewater treatment.

MS and NMR Analysis of Isotopically Labeled Chloramination Disinfection Byproducts: Hyperlinks and Chemical Reactions

FT-ICR MS and NMR analysis of an isotopically labeled complex mixture of water disinfection byproducts formed by chloramine disinfection of model phenolic acids is described. A new molecular formula assignment procedure using the CoreMS Python library able to assign isotopically enriched formulas is proposed. Statistical analysis of the assigned formulas showed that the number of compounds, the diversity of the mixture, and the chlorine count increase during the chloramination reaction. The complex reaction mixture was investigated as a network of reactions using PageRank and Reverse PageRank algorithms. Independent of the MS signal intensities, the PageRank algorithm calculates the formulas with the highest probability at convergence of the reaction; these were chlorinated and nitrated derivatives of the starting materials. The Reverse PageRank revealed that the most probable chemical transformations in the complex mixture were chlorination and decarboxylation. These agree with the data obtained from INADEQUATE NMR spectra and literature data, indicating that this approach could be applied to gain insight into reactions pathways taking place in complex mixtures without any prior knowledge.

Exhaled breath analysis in patients with potentially curative lung cancer undergoing surgery: a longitudinal study

Exhaled breath analysis has emerged as a non-invasive and promising method for early detection of lung cancer, offering a novel approach for diagnosis through the identification of specific biomarkers present in a patient's breath. For this longitudinal study, 29 treatment-naive patients with lung cancer were evaluated before and after surgery. Secondary electrospray ionization high-resolution mass spectrometry was used for exhaled breath analysis. Volatile organic compounds with absolute log2fold change ⩾1 andq-values ⩾ 0.71 were selected as potentially relevant. Exhaled breath analysis resulted in a total of 3482 features. 515 features showed a substantial difference before and after surgery. The small sample size generated a false positive rate of 0.71, therefore, around 154 of these 515 features were expected to be true changes. Biological identification of the features with the highest consistency (m/z-242.18428 andm/z-117.0539) revealed to potentially be 3-Oxotetradecanoic acid and Indole, respectively. Principal component analysis revealed a primary cluster of patients with a recurrent lung cancer, which remained undetected in the initial diagnostic and surgical procedures. The change of exhaled breath patterns after surgery in lung cancer emphasizes the potential for lung cancer screening and detection.

Association of Coffee Consumption and Prediagnostic Caffeine Metabolites With Incident Parkinson Disease in a Population-Based Cohort

BACKGROUND AND OBJECTIVES

Inverse associations between caffeine intake and Parkinson disease (PD) have been frequently implicated in human studies. However, no studies have quantified biomarkers of caffeine intake years before PD onset and investigated whether and which caffeine metabolites are related to PD.

METHODS

Associations between self-reported total coffee consumption and future PD risk were examined in the EPIC4PD study, a prospective population-based cohort including 6 European countries. Cases with PD were identified through medical records and reviewed by expert neurologists. Hazard ratios (HRs) and 95% CIs for coffee consumption and PD incidence were estimated using Cox proportional hazards models. A case-control study nested within the EPIC4PD was conducted, recruiting cases with incident PD and matching each case with a control by age, sex, study center, and fasting status at blood collection. Caffeine metabolites were quantified by high-resolution mass spectrometry in baseline collected plasma samples. Using conditional logistic regression models, odds ratios (ORs) and 95% CIs were estimated for caffeine metabolites and PD risk.

RESULTS

In the EPIC4PD cohort (comprising 184,024 individuals), the multivariable-adjusted HR comparing the highest coffee intake with nonconsumers was 0.63 (95% CI 0.46-0.88, p = 0.006). In the nested case-control study, which included 351 cases with incident PD and 351 matched controls, prediagnostic caffeine and its primary metabolites, paraxanthine and theophylline, were inversely associated with PD risk. The ORs were 0.80 (95% CI 0.67-0.95, p = 0.009), 0.82 (95% CI 0.69-0.96, p = 0.015), and 0.78 (95% CI 0.65-0.93, p = 0.005), respectively. Adjusting for smoking and alcohol consumption did not substantially change these results.

DISCUSSION

This study demonstrates that the neuroprotection of coffee on PD is attributed to caffeine and its metabolites by detailed quantification of plasma caffeine and its metabolites years before diagnosis.

Identification of novel biomarkers in the early diagnosis of malignant melanoma by untargeted liquid chromatography coupled to high-resolution mass spectrometry-based metabolomics: a pilot study

BACKGROUND

Malignant melanoma (MM) is a highly aggressive form of skin cancer whose incidence continues to rise worldwide. If diagnosed at an early stage, it has an excellent prognosis, but mortality increases significantly at advanced stages after distant spread. Unfortunately, early detection of aggressive melanoma remains a challenge.

OBJECTIVES

To identify novel blood-circulating biomarkers that may be useful in the diagnosis of MM to guide patient counselling and appropriate disease management.

METHODS

In this study, 105 serum samples from 26 healthy patients and 79 with MM were analysed using an untargeted approach by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) to compare the metabolomic profiles of both conditions. Resulting data were subjected to both univariate and multivariate statistical analysis to select robust biomarkers. The classification model obtained from this analysis was further validated with an independent cohort of 12 patients with stage I MM.

RESULTS

We successfully identified several lipidic metabolites differentially expressed in patients with stage I MM vs. healthy controls. Three of these metabolites were used to develop a classification model, which exhibited exceptional precision (0.92) and accuracy (0.94) when validated on an independent sample.

CONCLUSIONS

These results demonstrate that metabolomics using LC-HRMS is a powerful tool to identify and quantify metabolites in bodily fluids that could serve as potential early diagnostic markers for MM.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates, such as fatty acids, carbohydrates, lipids, and amino acids, to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities, which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection: direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF-MS/MS). Using DI-FTICR MS, 9644 metabolic features were detected where 7156 were assigned a molecular formula and 1107 were annotated by accurate mass assignment. Using LC-Q-TOF-MS/MS, 21,428 metabolic features were detected where 285 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 1340 heart metabolites were identified in this study, which span a wide range of polarities including polar (benzenoids, carbohydrates, and nucleosides) as well as nonpolar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results from this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Teaching mass spectrometry: A compilation of approaches to teaching theory and practice of mass spectrometry

The areas of mass spectrometry applications seem to be much larger than those of any other analytical techniques. They extend from the determination of molecular mass in organic chemistry, through the analytical applications in forensic, environmental and omics sciences, the application in extra-terrestrial exploration and many others. Mass spectrometry, usually coupled with chromatographic techniques, has also found wide application in the pharmaceutical industry, forensic laboratories, laboratories of sanitary inspection or environmental inspection, etc. The growing areas of applications give rise to the demand for the comprehensive mass spectrometry education of undergraduates. This overview covers the body of literature describing various interesting ideas that can be successfully used for teaching mass spectrometry. Since mass spectrometry is a multidisciplinary field, old but dynamically developing, teaching mass spectrometry may be more problematic in comparison to teaching other analytical techniques, for example, there is the problem of position of mass spectrometry in the chemistry curriculum. On the other hand, it is obvious that the mass spectrometry community, besides difficult scientific work, does great and admirable teaching work, in order to perfectly educate undergraduates in the field of mass spectrometry and to make learning mass spectrometry as attractive as possible.

Fire Impacts on the Soil Metabolome and Organic Matter Biodegradability

Global wildfire activity has increased since the 1970s and is projected to intensify throughout the 21st century. Wildfires change the composition and biodegradability of soil organic matter (SOM) which contains nutrients that fuel microbial metabolism. Though persistent forms of SOM often increase postfire, the response of more biodegradable SOM remains unclear. Here we simulated severe wildfires through a controlled "pyrocosm" approach to identify biodegradable sources of SOM and characterize the soil metabolome immediately postfire. Using microbial amplicon (16S/ITS) sequencing and gas chromatography-mass spectrometry, heterotrophic microbes (Actinobacteria, Firmicutes, and Protobacteria) and specific metabolites (glycine, protocatechuate, citric cycle intermediates) were enriched in burned soils, indicating that burned soils contain a variety of substrates that support microbial metabolism. Molecular formulas assigned by 21 T Fourier transform ion cyclotron resonance mass spectrometry showed that SOM in burned soil was lower in molecular weight and featured 20 to 43% more nitrogen-containing molecular formulas than unburned soil. We also measured higher water extractable organic carbon concentrations and higher CO2 efflux in burned soils. The observed enrichment of biodegradable SOM and microbial heterotrophs demonstrates the resilience of these soils to severe burning, providing important implications for postfire soil microbial and plant recolonization and ecosystem recovery.

Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one-third of the variation in these metabolite profiles is due to cross-cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site-to-site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal components. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender-related metabolic differences retains the interesting variance while reducing unwanted (site-related) variance.

Targeted Isolation of Antibiofilm Compounds from Halophytic Endophyte Bacillus velezensis 7NPB-3B Using LC-HR-MS-Based Metabolomics

The discovery of new natural products has become more challenging because of the re-isolation of compounds and the lack of new sources. Microbes dwelling in extreme conditions of high salinity and temperature are huge prospects for interesting natural metabolites. In this study, the endophytic bacteria Bacillus velezensis 7NPB-3B isolated from the halophyte Salicornia brachiata was screened for its biofilm inhibition against methicillin-resistant Staphylococcus aureus (MRSA). The fractionation of the crude extract was guided by bioassay and LC-HRMS-based metabolomics using multivariate analysis. The 37 fractions obtained by high-throughput chromatography were dereplicated using an in-house MS-Excel macro coupled with the Dictionary of Natural Products database. Successive bioactivity-guided separation yielded one novel compound (1), a diketopiperazine (m/z 469.258 [M - H]-) with an attached saturated decanoic acid chain, and four known compounds (2-5). The compounds were identified based on 1D- and 2D-NMR and mass spectrometry. Compounds 1 and 5 exhibited excellent biofilm inhibition properties of >90% against the MRSA pathogen at minimum inhibition concentrations of 25 and 35 µg/mL, respectively. The investigation resulted in the isolation of a novel diketopiperazine from a bacterial endophyte of an untapped plant using an omics approach.

An integrated screening method for paralytic shellfish toxins and their analogues based on fragmentation characteristics using an orbitrap-based ultrahigh-performance liquid chromatography-high-resolution mass spectrometry

Paralytic shellfish toxins (PSTs) perform a huge threat to food safety and public safety. In this study, an integrated non-targeted screening strategy was developed for the screening of PSTs and their analogues exploiting the fragmentation characteristics from ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). First, an extensible in-house PSTs compound database was developed. Second, the fragmentation characteristics of typical PSTs were studied and summarized using UHPLC-HRMS. Then, an integrated non-targeted screening strategy was developed based on fragmentation characteristics for screening of PSTs and their analogues. Finally, the method was fully validated in fortified shellfish samples and successfully applied to analyze the samples of OPCW exercise on biotoxin analysis. This promising approach can also be applied in a wide variety of scenarios, such as food safety, biotoxin verification, and forensic investigation.

Unambiguous Analysis and Systematic Mapping of Oxygenated Aromatic Compounds in Atmospheric Aerosols Using Ultrahigh-Resolution Mass Spectrometry

Compositional analysis of organic aerosols (OAs) at the molecular level has been a long-standing challenge in field and laboratory studies. In this work, we applied different extraction protocols to aerosol samples collected from the ambient atmosphere and biomass burning sources, followed by Orbitrap mass spectrometric analysis with a soft electrospray ionization source operating in both positive and negative ionization modes. To systematically map the distribution of mono- and dioxygenated aromatic compounds (referred to as aromatic CHO1 and CHO2 formulas) in OA, we developed a unique two-dimensional Kendrick mass defect (2D KMD) framework. Our analysis unveiled a total of (76, 64, 70) aromatic CHO1 formulas and (103, 110, 106) CHO2 formulas, corresponding to samples obtained from ambient air, rice straw burning, and sugarcane leaf burning, respectively. These results reveal a significant number of additional distinct formulas exclusively present in ambient samples, suggesting a significant chemical transformation of OAs in the atmosphere. The analytical approach can be further extended to incorporate multiple layers of 2D KMD, enabling systematic mapping of the unexplored chemical space for complex environmental samples.

UPLC-QTOF-MSE based metabolomics and chemometrics study of the pitaya processing

The search for knowledge related to the Pitaya (Hylocereus polyrhizus [F.A.C. Weber] Britton & Rose, family Cactaceae) is commonly due to its beneficial health properties e aesthetic values. But process to obtain pitaya pulp is a first and important step in providing information for the subsequent use of this fruit as colorant, for example. Therefore, the effects of the pulping process on the metabolomic and chemometric profile of non-volatile compounds of pitaya were assessed for the first time. The differences in metabolic fingerprints using UPLC-QTOF-MSE and multivariate modeling (PCA and OPLS-DA) was performed in the following treatments: treatment A, which consists of pelled pitaya and no ascorbic acid addition during pulping; treatment B, use of unpelled pitaya added of ascorbic acid during pulping; and control, unpelled pitaya and no ascorbic acid addition during pulping. For the metabolomic analysis, UPLC-QTOF-MSE shows an efficient method for the simultaneous determination of 35 non-volatile pitaya metabolites, including isorhamnetin glucosyl rhamnosyl isomers, phyllocactin isomers, 2'-O-apiosyl-phylocactin and 4'-O-malonyl-betanin. In addition, the chemometric analysis efficiently distinguished the metabolic compounds of each treatment applied and shows that the use of unpelled pitaya added of ascorbic acid during pulping has an interesting chemical profile due to the preservation or formation of compounds, such as those derived from betalain, and higher yields, which is desirable for the food industry.

ChloroDBPFinder: Machine Learning-Guided Recognition of Chlorinated Disinfection Byproducts from Nontargeted LC-HRMS Analysis

High-resolution mass spectrometry (HRMS) is a prominent analytical tool that characterizes chlorinated disinfection byproducts (Cl-DBPs) in an unbiased manner. Due to the diversity of chemicals, complex background signals, and the inherent analytical fluctuations of HRMS, conventional isotopic pattern (37Cl/35Cl), mass defect, and direct molecular formula (MF) prediction are insufficient for accurate recognition of the diverse Cl-DBPs in real environmental samples. This work proposes a novel strategy to recognize Cl-containing chemicals based on machine learning. Our hierarchical machine learning framework has two random forest-based models: the first layer is a binary classifier to recognize Cl-containing chemicals, and the second layer is a multiclass classifier to annotate the number of Cl present. This model was trained using ∼1.4 million distinctive MFs from PubChem. Evaluated on over 14,000 unique MFs from NIST20, this machine learning model achieved 93.3% accuracy in recognizing Cl-containing MFs (Cl-MFs) and 92.9% accuracy in annotating the number of Cl for Cl-MFs. Furthermore, the trained model was integrated into ChloroDBPFinder, a standalone R package for the streamlined processing of LC-HRMS data and annotating both known and unknown Cl-containing compounds. Tested on existing Cl-DBP data sets related to aspartame chlorination in tap water, our ChloroDBPFinder efficiently extracted 159 Cl-containing DBP features and tentatively annotated the structures of 10 Cl-DBPs via molecular networking. In another application of a chlorinated humic substance, ChloroDBPFinder extracted 79 high-quality Cl-DBPs and tentatively annotated six compounds. In summary, our proposed machine learning strategy and the developed ChloroDBPFinder provide an advanced solution to identifying Cl-containing compounds in nontargeted analysis of water samples. It is freely available on GitHub (https://github.com/HuanLab/ChloroDBPFinder).

Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data

Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing data sets for positive and negative modes. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favorable for quality control (QC) sample similarity, sample classification, and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.

MSdeCIpher: A Tool to Link Data from Complementary Ionization Techniques in High-Resolution GC-MS to Identify Molecular Ions

Electron ionization (EI) and molecular ion-generating techniques like chemical ionization (CI) are complementary ionization methods in gas chromatography (GC)-mass spectrometry (MS). However, manual curation effort and expert knowledge are required to correctly assign molecular ions to fragment spectra. MSdeCIpher is a software tool that enables the combination of two separate datasets from fragment-rich spectra, like EI-spectra, and soft ionization spectra containing molecular ion candidates. Using high-resolution GC-MS data, it identifies and assigns molecular ions based on retention time matching, user-defined adduct/neutral loss criteria, and sum formula matching. To our knowledge, no other freely available or vendor tool is currently capable of combining fragment-rich and soft ionization datasets in this manner. The tool's performance was evaluated on three test datasets. When molecular ions are present, MSdeCIpher consistently ranks the correct molecular ion for each fragment spectrum in one of the top positions, with average ranks of 1.5, 1, and 1.2 in the three datasets, respectively. MSdeCIpher effectively reduces candidate molecular ions for each fragment spectrum and thus enables the usage of compound identification tools that require molecular masses as input. It paves the way towards rapid annotations in untargeted analysis with high-resolution GC-MS.

Short-term and repeated exposure to particulate matter sizes from Imperial Valley, California to induce inflammation and asthmatic-like symptoms in mice

Imperial Valley, California has become increasingly hot, dry, and polluted over the past decade. Particulate matter (PM) levels are amongst the highest in this State, associated with significantly higher asthma prevalence among children in the region compared to national and state averages. The present study was performed to test the hypothesis that Imperial Valley PM by size and chemical composition might possess allergenic properties following introduction into murine lungs without prior sensitization to a known allergen with size fraction as a determining factor. In acute exposure experiments, BALB/c male mice were administered a single 50-μl oropharyngeal aspiration of nanopure water (H2O; control) or a stock 1 μg/μl PM solution. In sub-acute exposure experiments, male and female mice were treated with a total of six 16.6-μl intranasal instillations of H2O or stock PM solution over the course of 14 days. In all experiments, pulmonary function tests were performed 24 hr after the final instillation followed by necropsies for the collection of biological samples. Inflammatory responses measured via cellularity in histopathological tissue sections as well as significant, marked influxes of eosinophils and lymphocytes were noted in the bronchoalveolar lavage fluid in mice administered PM compared to control. Allergic responses, including airway hyperresponsiveness and significantly increased expression of IL-1ß, were found in male mice exposed to either PM2.5 or ultrafine (PMUF). A combination of all three size fractions of PM from Imperial Valley initiated atopic and asthmatic-like symptoms in the lungs of mice in the absence of additional allergen or preexisting condition.

OrbiFragsNets. A tool for automatic annotation of orbitrap MS2 spectra using networks grade as selection criteria

We introduce OrbiFragsNets, a tool for automatic annotation of MS2 spectra generated by Orbitrap instruments, as well as the concepts of chemical consistency and fragments networks. OrbiFragsNets takes advantage of the specific confidence interval for each peak in every MS2 spectrum, which is an unclear idea across the high-resolution mass spectrometry literature. The spectrum annotations are expressed as fragments networks, a set of networks with the possible combinations of annotations for the fragments. The model behind OrbiFragsNets is briefly described here and explained in detail in the constantly updated manual available in the GitHub repository. This new approach in MS2 spectrum de novo automatic annotation proved to perform as good as well established tools such as RMassBank and SIRIUS.•A new approach on automatic annotation of Orbitrap MS2 spectra is introduced.•Possible spectrum annotation are described as independent consistent networks, with annotations for each fragment as nodes, and annotations for the mass difference between fragments as edges.•Annotation process is described as the selection of the most connected fragments network.